A method using deep reinforcement learning (DRL) to non-iteratively generate an optimal mesh for an arbitrary blade passage is developed. Despite automation in mesh generation using either an empirical approach or an optimization algorithm, repeated tuning of meshing parameters is still required for a new geometry. The method developed herein employs a DRL-based multi-condition optimization technique to define optimal meshing parameters as a function of the blade geometry, attaining automation, minimization of human intervention, and computational efficiency. The meshing parameters are optimized by training an elliptic mesh generator which generates a structured mesh for a blade passage with an arbitrary blade geometry. During each episode of the DRL process, the mesh generator is trained to produce an optimal mesh for a randomly selected blade passage by updating the meshing parameters until the mesh quality, as measured by the ratio of determinants of the Jacobian matrices and the skewness, reaches the highest level. Once the training is completed, the mesh generator create an optimal mesh for a new arbitrary blade passage in a single try without an repetitive process for the parameter tuning for mesh generation from the scratch. The effectiveness and robustness of the proposed method are demonstrated through the generation of meshes for various blade passages.

翻译：本文提出了一种基于深度强化学习（DRL）的非迭代生成任意叶片通道最优网格的方法。尽管通过经验方法或优化算法实现了网格生成的自动化，但对于新的几何形状，仍需要反复调整网格参数。本文开发的方法采用基于DRL的多条件优化技术，将最优网格参数定义为叶片几何形状的函数，从而实现自动化、最小化人工干预并提高计算效率。通过训练椭圆网格生成器对网格参数进行优化，该生成器可为具有任意叶片几何形状的叶片通道生成结构化网格。在DRL过程的每个周期中，网格生成器通过更新网格参数进行训练，以生成随机选取的叶片通道的最优网格，直至网格质量（以雅可比矩阵行列式比值和偏斜度衡量）达到最高水平。训练完成后，网格生成器可一步生成任意新叶片通道的最优网格，无需从零开始反复调整网格参数。通过生成多种叶片通道的网格，验证了该方法的有效性和鲁棒性。